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This repository will contain all the presentations, content, hands-on notebooks for a full day Generative AI workshop on Training, Fine-tuning Large Language Models for the DataHack Summit 2024 conference.
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# Mastering Large Language Models: Training and Fine-Tuning Large Language Models Workshop (DHS 2024) This repository contains all the presentations and hands-on code notebooks for the "Training and Fine-Tuning Large Language Models" workshop held at [Analytics Vidhya DataHack Summit 2024](https://www.analyticsvidhya.com/datahacksummit/workshops/mastering-llms-training-fine-tuning-and-best-practices). The workshop is divided into several modules, each focusing on different aspects of working with large language models (LLMs) from prompting to fine-tuning __Workshop instructor:__ [Dipanjan (DJ) Sarkar](https://www.linkedin.com/in/dipanjans/) <br/> ## Tools and Frameworks Used <div> <img align="left" width="150" src="https://i.imgur.com/Eoxjs0J.png" hspace="10"/> <img align="left" width="100" src="https://i.imgur.com/KhQJXz3.png" hspace="10"/> <img align="left" width="150" src="https://i.imgur.com/1ZMOoc7.png" hspace="10"/> </div> <br clear="left"/><br/> <div> <img align="left" width="300" src="https://i.imgur.com/rsw8Wjb.png" hspace="0"/> <img align="left" width="160" src="https://i.imgur.com/fd7fi09.png" hspace="0"/> </div> <br clear="left"/> <br/> ## Server Configuration Setting - I have used [Runpod](https://www.runpod.io/) with A40 NVIDIA GPUs with 48GB VRAM. - Also the software stack used was PyTorch 2.4.0, Python 3.11, CUDA 12.4 and Ubuntu 22.04. - Try to keep the stack as similar to prevent library issues.  ## Workshop Overview This workshop is divided into 5 modules, the following graphic clearly showcases what is covered in each module. It is recommended to navigate sequentially through each module to get the best experience. Also do go through the presentation decks to learn the conceptual aspects of the topics besides trying out the hands-on python notebooks. __Note:__ For environment setup each module has its own `requirements.ipynb` notebook with all the necessary libraries needed to be installed. We use [Runpod.io](https://www.runpod.io/) A40 Servers which give us a 48GB VRAM GPU which is enough for these experiments. Make sure you have a separate disk volume of at least 30GB to store the Llama 3 LLM weight files after fine-tuning, else you will get an error. ) <br/> ## Modules Overview ### Module 01: Transformers, LLMs, and Generative AI Essentials <img align="left" width="150" src="https://i.imgur.com/nnrHte5.png"/> <br/> This module serves as the foundational introduction to Transformers, Foundation models, and fine-tuned Large Language Models (LLMs). You'll learn about both simple and contextual embedding models and their applications in real-world scenarios. Practical exercises include building a custom search engine and leveraging Transformer-based LLMs for tasks such as sentiment analysis, Q&A, summarization, and zero-shot classification. A special focus is given to using Microsoft Phi-3 Mini Instruct locally for prompt engineering and comparing performance across models like GPT-4o mini and Meta Llama 3.1 8B. <br/><br/><br/> - **[Module_01_Install_Requirements.ipynb](https://github.com/dipanjanS/training-fine-tuning-large-language-models-workshop-dhs2024/blob/main/Module-01-Transformers-LLMs-Generative-AI-Essentials/Module_01_Install_Requirements.ipynb)**: A notebook to set up the required environment and dependencies for the exercises in this module. - **[Module_01_LC1_Simple_Word_Embedding_Models_Solutions.ipynb](https://github.com/dipanjanS/training-fine-tuning-large-language-models-workshop-dhs2024/blob/main/Module-01-Transformers-LLMs-Generative-AI-Essentials/Solutions/Module_01_LC1_Simple_Word_Embedding_Models_Solutions.ipynb)**: Introduction to simple word embedding models, providing hands-on exercises to understand their workings. - **[Module_01_LC2_Contextual_Embeddings_and_Semantic_Search_Engines_with_Transformers_Solutions](https://github.com/dipanjanS/training-fine-tuning-large-language-model
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matched fp:93dabedafb2459c1, name:fine-tuning, desc:fine-tuning, name:fine tuning